Spatial distribution of heavy metals in soils around cement factory and health risk assessment: a case study of Canakkale-Ezine (NW Turkey).

Sustainable use of agricultural land plays a crucial role in ensuring food security. For sustainable use of soils, it is very important to focus on the pollution status. This study was conducted on the soils in the northern part of the Ezine district in northwestern Turkey. The study aimed to determine the physicochemical properties of the soils in the vicinity of the cement plant, the concentrations of heavy metals, the spatial distribution of heavy metals, and their impact on the health of the local human population. Soil samples were collected from the cement plant in different directions (S,W, N, E, NE, SW) and at different distances (1, 3, 5, and 7 km) from 0-10 cm depth with three replicates. The soil samples were analyzed for texture, pH, electrical conductivity, lime, and heavy metals such as Cd, Co, Cu, Fe, Mn, Ni, Pb, and Zn. The soils had different textures (loam, sandy clay loam, loam, sandy loam), slightly alkaline pH, low lime content, and moderate organic matter content. Except for Cd and Pb, the average values of the other heavy metals (Co = 1.18 < 19 mg kg-1,Cr = 50.92 < 90 mg kg-1, Cu = 31.21 < 45 mg kg-1, Fe = 16,007 < 47,200 mg kg-1, Mn = 499.68 < 850 mg kg-1, Ni = 41.17 < 68 mg kg-1, Zn = 50.91 < 95 mg kg-1) in the soils were below the normal background level. The heavy metal contents of the soils in the study area are influenced by various sources (geological structure, agrochemicals used in agricultural activities, and vehicle traffic). The prevailing wind direction did not influence the local distribution of heavy metals in soils in the study area. The health risk assessment model studies showed that the hazard quotient values of less than 1 for adults and children indicate that the noncarcinogenic risks were insignificant. People exposed to heavy metals in the soils of the study area contaminated from various sources for a long time could be at carcinogenic risk. Since Cr and Pb exceed the acceptable risk range in children and Cr exceeds the acceptable risk range in adults, geochemical monitoring of soils should be conducted periodically by authorized institutions in the study area.

New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems.

For the estimation of the soil organic carbon stocks, bulk density (BD) is a fundamental parameter but measured data are usually not available especially when dealing with legacy soil data. It is possible to estimate BD by applying pedotransfer function (PTF). We applied different estimation methods with the aim to define a suitable PTF for BD of arable land for the Mediterranean Basin, which has peculiar climate features that may influence the soil carbon sequestration. To improve the existing BD estimation methods, we used a set of public climatic and topographic data along with the soil texture and organic carbon data. The present work consisted of the following steps: i) development of three PTFs models separately for top (0-0.4 m) and subsoil (0.4-1.2 m), ii) a 10-fold cross-validation, iii) model transferability using an external dataset derived from published data. The development of the new PTFs was based on the training dataset consisting of World Soil Information Service (WoSIS) soil profile data, climatic data from WorldClim at 1 km spatial resolution and Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m spatial resolution. The three PTFs models were developed using: Multiple Linear Regression stepwise (MLR-S), Multiple Linear Regression backward stepwise (MLR-BS), and Artificial Neural Network (ANN). The predictions of the newly developed PTFs were compared with the BD calculated using the PTF proposed by Manrique and Jones (MJ) and the modelled BD derived from the global SoilGrids dataset. For the topsoil training dataset (N = 129), MLR-S, MLR-BS and ANN had a R2 0.35, 0.58 and 0.86, respectively. For the model transferability, the three PTFs applied to the external topsoil dataset (N = 59), achieved R2 values of 0.06, 0.03 and 0.41. For the subsoil training dataset (N = 180), MLR-S, MLR-BS and ANN the R2 values were 0.36, 0.46 and 0.83, respectively. When applied to the external subsoil dataset (N = 29), the R2 values were 0.05, 0.06 and 0.41. The cross-validation for both top and subsoil dataset, resulted in an intermediate performance compared to calibration and validation with the external dataset. The new ANN PTF outperformed MLR-S, MLR-BS, MJ and SoilGrids approaches for estimating BD. Further improvements may be achieved by additionally considering the time of sampling, agricultural soil management and cultivation practices in predictive models.